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For locomotion, is an arm on a legged robot a liability or an asset for locomotion? Biological systems evolved additional limbs beyond legs that facilitates postural control.
S. P. Singh, “Transfer of learning by composing solutions of elemental sequential tasks,”
1992
Earlier work this paper cites.
J. Peng and R. J. Williams, “Incremental multi-step q-learning,” in
1994
Earlier work this paper cites.
T. Dean and S.-H. Lin, “Decomposition techniques for planning in stochastic domains,” in
1995
Earlier work this paper cites.
S. Schaal, “Learning from demonstration,”
1996
Earlier work this paper cites.
C. G. Atkeson and S. Schaal, “Robot learning from demonstration,” in
1997
Earlier work this paper cites.
C. Walker, C. J. Vierck Jr, and L. A. Ritz, “Balance in the cat: role of the tail and effects of sacrocaudal transection,”
1998
Earlier work this paper cites.
T. G. Dietterich, “Hierarchical reinforcement learning with the maxq value function decomposition,”
2000
Earlier work this paper cites.
A. L. Gibbs and F. E. Su, “On choosing and bounding probability metrics,”
2002
Earlier work this paper cites.
L. H. Ting and J. L. McKay, “Neuromechanics of muscle synergies for posture and movement,”
2007
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in
2011
Earlier work this paper cites.
A. M. Johnson, T. Libby, E. Chang-Siu, M. Tomizuka, R. J. Full, and D. E. Koditschek, “Tail assisted dynamic self righting,” in
2012
Earlier work this paper cites.
A. Patel and M. Braae, “Rapid turning at high-speed: Inspirations from the cheetah’s tail,” in
2013
Earlier work this paper cites.
E. Chang-Siu, T. Libby, M. Brown, R. J. Full, and M. Tomizuka, “A nonlinear feedback controller for aerial self-righting by a tailed robot,” in
2013
Earlier work this paper cites.
J. Zhao, T. Zhao, N. Xi, F. J. Cintrón, M. W. Mutka, and L. Xiao, “Controlling aerial maneuvering of a miniature jumping robot using its tail,” in
2013
Earlier work this paper cites.
B. McInroe, H. C. Astley, C. Gong, S. M. Kawano, P. E. Schiebel, J. M. Rieser, H. Choset, R. W. Blob, and D. I. Goldman, “Tail use improves performance on soft substrates in models of early vertebrate land locomotors,”
2016
Earlier work this paper cites.
T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband,
2018
Earlier work this paper cites.
X. B. Peng, P. Abbeel, S. Levine, and M. Van de Panne, “Deepmimic: Example-guided deep reinforcement learning of physics-based character skills,”
2018
Earlier work this paper cites.
C. D. Bellicoso, K. Krämer, M. Stäuble, D. Sako, F. Jenelten, M. Bjelonic, and M. Hutter, “Alma-articulated locomotion and manipulation for a torque-controllable robot,” in
2019
Cited alongside, same era.
2019
Cited alongside, same era.
J. Nabeshima, m. y. Saraiji, and K. Minamizawa, “Arque: artificial biomimicry-inspired tail for extending innate body functions,” 07 2019, pp. 1–2
2019
Cited alongside, same era.
Z. Wang, H.-X. Li, and C. Chen, “Incremental reinforcement learning in continuous spaces via policy relaxation and importance weighting,”
2019
Cited alongside, same era.
A. Loquercio, E. Kaufmann, R. Ranftl, A. Dosovitskiy, V. Koltun, and D. Scaramuzza, “Deep drone racing: From simulation to reality with domain randomization,”
Z. Wang, C. Chen, and D. Dong, “Lifelong incremental reinforcement learning with online bayesian inference,”
2021
Later among the works it cites.
A. Loquercio, E. Kaufmann, R. Ranftl, M. Müller, V. Koltun, and D. Scaramuzza, “Learning high-speed flight in the wild,”
2021
Later among the works it cites.
2021
Later among the works it cites.
Z. Fu, X. Cheng, and D. Pathak, “Deep whole-body control: Learning a unified policy for manipulation and locomotion,” in
2022
Later among the works it cites.
J.-R. Chiu, J.-P. Sleiman, M. Mittal, F. Farshidian, and M. Hutter, “A collision-free mpc for whole-body dynamic locomotion and manipulation,” in
2022
Later among the works it cites.
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2019
Cited alongside, same era.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,”
2020
Cited alongside, same era.
X. B. Peng, E. Coumans, T. Zhang, T.-W. E. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” in
2020
Cited alongside, same era.
J. An, T. Chung, C. H. D. Lo, C. Ma, X. Chu, and K. S. Au, “Development of a bipedal hopping robot with morphable inertial tail for agile locomotion,” in
2020
Cited alongside, same era.
H. Ferrolho, W. Merkt, V. Ivan, W. Wolfslag, and S. Vijayakumar, “Optimizing dynamic trajectories for robustness to disturbances using polytopic projections,” in
2020
Cited alongside, same era.
Z. Xie, P. Clary, J. Dao, P. Morais, J. Hurst, and M. van de Panne, “Learning locomotion skills for cassie: Iterative design and sim-to-real,” in
2020
Cited alongside, same era.
J.-P. Sleiman, F. Farshidian, M. V. Minniti, and M. Hutter, “A unified mpc framework for whole-body dynamic locomotion and manipulation,”
2021
Cited alongside, same era.
S. Zimmermann, R. Poranne, and S. Coros, “Go fetch!-dynamic grasps using boston dynamics spot with external robotic arm,” in
2021
Cited alongside, same era.
A. Loquercio, A. Kumar, and J. Malik, “Learning visual locomotion with cross-modal supervision,”
2022
Later among the works it cites.
A. Agarwal, A. Kumar, J. Malik, and D. Pathak, “Legged locomotion in challenging terrains using egocentric vision,” 2022. [Online]. Available:
2022
Later among the works it cites.
T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,”
2022
Later among the works it cites.
D. Soto, “Simplifying robotic locomotion by escaping traps via an active tail,” Master’s thesis, Georgia Institute of Technology, 2022
2022
Later among the works it cites.
C. Khazoom and S. Kim, “Humanoid arm motion planning for improved disturbance recovery using model hierarchy predictive control,” in
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Ma, F. Farshidian, T. Miki, J. Lee, and M. Hutter, “Combining learning-based locomotion policy with model-based manipulation for legged mobile manipulators,”
2022
Later among the works it cites.
R. C. Quesada and Y. Demiris, “Holo-spok: Affordance-aware augmented reality control of legged manipulators,” in
2022
Later among the works it cites.
J.-P. Sleiman, F. Farshidian, and M. Hutter, “Versatile multicontact planning and control for legged loco-manipulation,”
2023
Closest in time.
Y. Ma, F. Farshidian, and M. Hutter, “Learning arm-assisted fall damage reduction and recovery for legged mobile manipulators,” in
2023
Closest in time.
N. Kumar, T. Silver, W. McClinton, L. Zhao, S. Proulx, T. Lozano-Pérez, L. P. Kaelbling, and J. Barry, “Practice makes perfect: Planning to learn skill parameter policies,” 2024
2024
Closest in time.